In an era where healthcare is rapidly transforming, diagnostics has emerged as far more than a supporting function it has become the foundation of modern medicine. This central theme unfolded in a recent episode of the InnoHealth Magazine Podcast, where host Dr. Soumya Singh engaged in a deep, nuanced conversation with Dr. Shelly Mahajan, Lab Director & Clinical Head – Genomics at Mahajan Imaging & Labs.

The discussion moved beyond innovation buzzwords to examine the real-world impact of genomics, artificial intelligence, standardization, ethics, and most importantly patient trust.

Diagnostics Moves From the Background to the Frontline

For decades, diagnostics functioned quietly in the background of healthcare. Patients rarely interacted with diagnosticians, and lab reports were often viewed as secondary confirmations of a clinician’s judgment. According to Dr. Mahajan, that perception has fundamentally changed.

“Diagnostics is no longer about confirming illness. It is about predicting disease, preventing it, and managing a person’s entire health journey.”

This shift reflects a broader transformation in healthcare itself. Preventive screenings, wellness monitoring, and early risk assessment now rely heavily on diagnostics. Individuals today engage with testing not only when they are sick, but also when they want to stay healthy making diagnostics a part of everyday life rather than episodic care.

Molecular Diagnostics and the Democratization of Precision

One of the most powerful drivers of this change has been molecular diagnostics. What was once considered niche or highly specialized is now increasingly accessible to routine patients. Dr. Mahajan highlighted how PCR and genomic testing have become household terms, especially following the COVID-19 pandemic.

“Today, we can predict susceptibility to disease by testing DNA from a simple blood or saliva sample and that has completely changed how early we can intervene.”

Molecular diagnostics has revolutionized disease detection across oncology, infectious diseases, and autoimmune disorders. The ability to diagnose earlier, predict risk, and tailor treatment plans has pushed healthcare closer to the ideal of personalized medicine—where therapy is shaped around the individual rather than the average patient.

Artificial Intelligence: Acceleration With Accountability

Artificial intelligence has emerged as a powerful force within diagnostics, improving efficiency, speed, and accuracy in radiology and pathology. AI-powered tools can now screen pathology slides, assist radiologists in detecting early cancers, and reduce diagnostic delays—particularly in regions with limited access to specialists.

However, Dr. Mahajan was unequivocal in her caution: AI is a tool, not a decision-maker.

“AI can screen faster and reduce errors, but it cannot replace clinical judgment. Without human oversight, even the smartest algorithm can misdiagnose.”

She emphasized that AI systems are only as good as the data on which they are trained. A lack of diversity or quality in training datasets can introduce bias, leading to skewed results that directly impact patient care. Continuous validation, transparency, and regulatory oversight are therefore critical to safe AI adoption.

Standardization: The Backbone of Credibility

One of the most pressing concerns raised in the conversation was the lack of uniformity across diagnostic laboratories. Variations in reference ranges, methodologies, and reporting standards often leave patients confused and clinicians uncertain.

Dr. Mahajan stressed that accreditation, adherence to international quality standards, regular audits, calibrated equipment, and continuous staff training are not optional—they are fundamental. Without standardization, diagnostics risks becoming volume-driven rather than value-driven, leading to over-testing, patient anxiety, and unnecessary healthcare costs.

Over-Testing and the Need for Value-Based Diagnostics

While access to diagnostics has improved, it has also created a paradox: more testing does not always mean better care. Over-testing can result in false positives, misdiagnosis, and emotional distress for patients.

Dr. Mahajan advocated for stronger collaboration between clinicians and diagnosticians, evidence-based testing protocols, and better patient education. The goal, she explained, is smarter diagnostics, not more diagnostics.

“We need to move away from volume-based diagnosis and focus on tests that actually add value to a patient’s treatment journey.”

Transparency, Ethics, and Rebuilding Patient Trust

Addressing recent controversies around lab practices and data integrity, Dr. Mahajan underscored that trust is fragile—and once lost, difficult to rebuild. Transparency, she argued, is the most powerful tool laboratories have.

She noted a positive cultural shift: patients now actively want to speak with diagnosticians, discuss reports, and understand their health data. This growing engagement reflects a new era where patients expect not just accuracy, but accountability and empathy.

The Road Ahead: Technology With a Human Core

Looking forward, Dr. Mahajan expressed optimism about emerging breakthroughs such as multi-omics diagnostics, AI-assisted tools for rural healthcare, and continuous monitoring through wearables. These innovations promise earlier detection, ultra-personalized treatment, and improved access.

Yet the podcast made one truth abundantly clear: technology alone cannot define the future of diagnostics.

The future will belong to systems that combine scientific excellence, ethical practice, rigorous quality standards, and human judgment—where every report carries not just data, but responsibility.

As this episode of the InnoHealth Magazine Podcast demonstrates, diagnostics is no longer just about tests. It is about trust, transparency, and putting patients at the heart of innovation. 

The post Diagnostics at the Frontline of Modern Healthcare: From Disease Detection to Prevention appeared first on InnoHEALTH magazine.

A major concern lies in the fact that the majority of current genomic datasets are derived predominantly from individuals of European ancestry, resulting in a significant underrepresentation of data from other ethnicities—particularly minority and diverse communities. This imbalance has deepened existing global health inequities, leading to reduced diagnostic accuracy, compromised effectiveness of precision medicine, and uneven access to personalized healthcare solutions.

As a result, these disparities further exacerbate socio-economic inequality, with serious ramifications for healthcare systems in India and other low- and middle-income nations. Bridging this gap is now an urgent imperative for medical professionals and health policy leaders working in these regions.

India, home to over 1.4 billion people and accounting for around 18% of the global population, ranks as the most populated country in the world. The nation is known for its vast diversity, comprising over 4600 anthropologically distinct groups. These populations are classified based on caste, tribe, and religion, with variations in cultural practices, geography, climate, physical traits, marital customs, languages, and genetic makeup. Historically, India served as a key route for early human migration out of Africa via its southern coasts. Over time, multiple waves of migration and invasions have further enriched the genetic diversity of the Indian subcontinent.

Despite its vast genetic diversity, India remains markedly underrepresented in global genomic research. The population is characterized by pronounced stratification into numerous endogamous communities and elevated levels of consanguinity, which contribute to a higher incidence of recessive genetic disorders. Yet, the absence of large-scale, India-focused genome sequencing initiatives has led to a significant gap in the documentation of subpopulation-specific genetic variants within international medical literature and research efforts.

Over the last decade, advancements in next-generation sequencing (NGS) technologies and their increasing affordability have significantly transformed the understanding of genetic variations in populations worldwide. Global initiatives such as the 1000 Genomes Project, ExAC (Exome Aggregation Consortium), ESP6500 (Exome Sequencing Project version 6500), and gnomAD (Genome Aggregation Database) have contributed to the development of extensive reference and patient genome datasets across continents. While these datasets include genomes from individuals of Indian origin, the sample size is insufficient to capture the vast genetic diversity and heterogeneity of the Indian population.

In addition to these global efforts, a few studies specific to the Asian and Indian populations have been conducted to explore the genetic landscape in this region. For instance, the Indian Genome Variation (IGV) Consortium examined over 1800 individuals from 55 subpopulations across 900 genes. This study highlighted the genetic heterogeneity of the Indian population and identified unique founder mutations within the subcontinent, improving the understanding of genotype-phenotype relationships. Similarly, the GenomeAsia100K project addressed questions related to Asian populations, including 598 Indian samples primarily representing tribal groups and specific castes, predominantly from southern India.

Given India’s population size, these datasets represent only a small portion of the country’s genetic diversity. To better understand this diversity, comprehensive genome sequencing efforts must focus on the cultural, ethnic, and geographic variation within India. Such population-specific studies can facilitate the identification of genetic variants and polymorphisms linked to diseases, enhance the effectiveness of precision medicine, create robust population-specific reference genome datasets, and improve clinical predictions.

The Indian clinical genomics database IndiGen has garnered significant attention for its efforts in cataloguing genome sequencing data, particularly for rare genetic disorders. Other initiatives spearheaded by the Department of Biotechnology, such as the 10,000 Genome Sequencing Project (GenomeIndia), the National Inherited Disorders Administration Kendras (NIDAN), and the Unique Methods of Management of Inherited Disorders (UMMID), are actively working to document population-level genetic variations. The GenomeIndia project aims to generate a reference database of genetic variations specific to the Indian population by sequencing the genomes of 10,000 individuals from 99 diverse ethnic groups, provide open-access genome data for academic and research purposes through the IBDC (Indian Biological Data Centre) platform, develop genome-wide and disease-specific genetic chips for affordable diagnostics and research, and ultimately, lay the foundation for genome-based precision medicine in India.

In the first phase of the GenomeIndia project, joint variant calling was conducted on 5750 samples, representing 69 distinct population groups across India, highlighting the genetic complexity of the Indian population and the necessity of a large-scale initiative like GenomeIndia.

From a dataset comprising 5,750 samples, over 135 million genetic variants were identified—primarily single nucleotide variants (SNVs) and small insertions or deletions (INDELs), with a smaller subset consisting of multi-allelic variants. While a majority of these (~65%) were classified as ultra-rare, more than 6.9 million common variants (approximately 11% of the total) were detected. These widely shared variants across Indian subpopulations offer significant promise for genome-wide association studies aimed at identifying genetic determinants of prevalent traits. Moreover, they are instrumental in refining gene chips customized to reflect the genetic landscape of the Indian population, as many of these variants are either rare or absent in global variant databases.

A substantial proportion of the discovered variants has been functionally annotated. Although the majority of SNVs and INDELs lie within non-coding regions, more than 1.4 million variants were identified as potentially functional. These include missense mutations, frameshift variants, splice site alterations, and changes within untranslated regions—each of which may impact gene expression and phenotypic outcomes. Notably, novel missense variants in the LDLR (Low-Density Lipoprotein Receptor) gene, associated with familial hypercholesterolemia, were identified, underscoring their clinical significance within the Indian context.

The genetic data also provides a unique perspective on India’s population history and linguistic diversity, capturing groups from the four major linguistic families: Indo-European, Dravidian, Austro-Asiatic, and Tibeto-Burman. This unprecedented genomic diversity contributes to a deeper understanding of the population dynamics and history within the Indian subcontinent.

The private genetic and genomic laboratories in India hold a crucial role in this regard. These laboratories typically offer a range of services, including molecular and cytogenetic analysis, single-gene mutation testing, multi-gene panel testing, targeted or whole-exome sequencing, and whole-genome sequencing. Some laboratories also specialize in areas such as fetal medicine, prenatal diagnostics, rare genetic disorders, and familial cancer. While many of these laboratories boast state-of-the-art equipment and technical facilities, only a limited number have skilled bioinformatics support for managing and interpreting sequencing data.

A critical challenge faced by these laboratories is the lack of standardized protocols to interpret variants of unknown significance (VUS), which is crucial for determining their potential pathogenicity. Moreover, certain pre-existing “founder mutations” prevalent in India’s diverse populations are often misclassified as VUS, thereby depriving affected individuals of appropriate clinical interventions.

To address some of these challenges, mandatory data sharing is required by all the commercial and non-commercial laboratories. Standardized annotation consistent with international nomenclature and classification is an important step to improve data consistency and reliability. The shared genomic database should be accessible to all, regardless of geographical location, to foster inclusivity.

Many profit-driven genome laboratories in India and other low- and middle-income countries are affiliated with multinational organizations or overseas laboratories. Their datasets are often based on individuals of European descent or small, non-representative minority groups, leading to potentially flawed interpretations of genomic variants. This issue is particularly critical, where indigenous populations risk being marginalized in genomic healthcare.

To address this, international collaboration, led by organizations such as the World Health Organization (WHO) and the Global Genome Consortium, is essential. These efforts could provide technical guidance, encourage data sharing, and help establish centralized genome databases. Such initiatives would not only advance precision medicine but also yield significant socio-economic benefits by making genomic healthcare more equitable and accessible.

Mandatory data sharing through centralized data centres and robust regulation, coupled with international cooperation, are imperative to ensure the effective integration of genomic medicine into healthcare systems in India while addressing global health disparities.

Author’s Biography

Sombuddha Roy Bhowmick is a driven professional with a strong foundation in data analysis and bioinformatics. He has experience developing innovative solutions across diverse fields, including genomics, machine learning, and clinical data management. 

The post Are We Missing the Genetic Puzzle in India’s Healthcare? appeared first on InnoHEALTH magazine.

Climate change is no longer just an environmental issue; it is a significant public health concern. The increasing frequency of extreme weather events, rising global temperatures, and shifting ecosystems are contributing to a surge in vector-borne diseases, respiratory illnesses, and other health crises. Dr. Jasprit Kaur Dhanjal, an Assistant Professor in the Department of Computational Biology at IIIT Delhi, is at the forefront of addressing these challenges through data science and health informatics.

At the InnoHEALTH Conference 2024, she highlighted the urgent need to bridge the gap between climate and health data to develop predictive models that can aid in precision medicine and disease prevention.

Understanding the Connection Between Climate Change and Health

Dr. Dhanjal emphasized that climate change has a direct impact on human health. Some of the most pressing issues include:

• Vector-Borne Diseases – Rising temperatures and unpredictable rainfall patterns have led to an increase in mosquito-borne diseases like malaria, dengue, and chikungunya.
• Respiratory Disorders – Increased air pollution and changing weather conditions have contributed to higher rates of asthma and chronic obstructive pulmonary disease (COPD).
• Food and Water Scarcity – Climate change affects agricultural yields and water availability, leading to malnutrition and waterborne illnesses.
• Heat-Related Illnesses – Extreme heat waves pose a significant threat, especially to the elderly and individuals with pre-existing conditions.

The Role of Data Science in Climate and Health Research

One of the key gaps in current climate-health research is the lack of comprehensive, integrated datasets. According to Dr. Dhanjal, there is an urgent need for collaborative data-sharing platforms where climate scientists, health professionals, and policymakers can work together to analyze trends and make informed decisions.

Data science can play a transformative role in:

• Predicting Disease Outbreaks – Using machine learning models to identify patterns in disease spread based on environmental factors.
• Analyzing Climate Trends – Studying historical and real-time climate data to forecast future health risks.
• Creating Early Warning Systems – Developing systems that alert authorities to potential public health threats before they escalate.
• Improving Healthcare Planning – Ensuring hospitals and healthcare systems are prepared for climate-induced health emergencies.

Bridging the Gap Between Health and Climate Science

Dr. Dhanjal is actively working on initiatives to train professionals in both health and climate data analysis. One such initiative is a PG Diploma Program in Data Science for Health and Climate, which aims to equip researchers with the necessary technical skills to interpret and utilize climate-health datasets effectively.

She pointed out that many professionals working in either field do not fully understand the challenges of the other. By fostering collaboration among health experts, climatologists, and data scientists, the academic community can develop comprehensive solutions to mitigate health risks associated with climate change.

The Need for Policy Interventions and Global Collaboration

To tackle the growing health risks posed by climate change, governments and policymakers must take proactive measures:

• Enhancing Data Infrastructure – Investing in national and global health-climate databases for better decision-making.
• Integrating Climate Health Policies – Ensuring health and climate policies are aligned to address long-term risks.
• Promoting Green Healthcare Initiatives – Encouraging hospitals to adopt renewable energy sources and reduce their carbon footprint.
• Strengthening Global Health Networks – Facilitating international collaborations to share research and best practices.

A Message to Future Data Scientists and Health Researchers

Dr. Dhanjal urges young researchers and students to leverage technology in addressing climate and health challenges. She believes that with the right skills, the next generation of data scientists can play a crucial role in shaping sustainable, data-driven healthcare solutions.

Her message is clear: Technology, collaboration, and proactive policy measures are key to creating a healthier, more resilient future. As climate change continues to affect global health, data science and interdisciplinary research will be vital tools in combating its adverse effects.

Conclusion

The link between climate change and health is undeniable, and addressing it requires a multi-disciplinary approach. Through big data analytics, predictive modeling, and collaborative policymaking, researchers like Dr. Jasprit Kaur Dhanjal are paving the way for precision medicine and early disease intervention.

As the world grapples with unprecedented climate challenges, integrating data science with healthcare solutions will be essential in safeguarding public health for future generations.

Composed By

InnoHEALTH magazine digital team

The post Climate Change and Health: The Role of Data Science in Addressing Global Challenges appeared first on InnoHEALTH magazine.

At the InnoHEALTH Conference 2024, I had the privilege of speaking with Dr. Ravi Gaur, a renowned onco-pathologist with four decades of experience in diagnostics and precision medicine. Our discussion covered the impact of artificial intelligence (AI), healthcare accessibility, and the future of medical innovation in India.

AI in Healthcare: Friend or Foe?

With the rise of AI, many people worry that it might replace human intelligence. However, Dr. Gaur had a refreshing take on this.

“AI will not replace human intelligence; it will enhance it. It’s not artificial intelligence—it’s augmented intelligence. AI, when used correctly, can amplify human skills, improve precision, and drive innovation in healthcare.”

While AI is transforming personalized medicine, diagnostics, and treatment plans, he emphasized that the key is collaboration between AI and human expertise.

“If we don’t adopt AI, we will fall behind. The goal is not to fear AI but to use it to create what I call ‘awesome intelligence’—a fusion of human intuition and machine efficiency.”

Bringing AI to Government Healthcare: A Slow but Promising Journey

Unlike the private sector, where AI-driven technologies are quickly being implemented, government healthcare systems often face bureaucratic hurdles and slower regulatory processes.

Dr. Gaur, who has worked closely with both the private sector and government, explained the challenge:

“Technology evolves rapidly—think about how your smartphone gets updates every few weeks. But government regulations are reviewed every 3–5 years. By the time policies are updated, the technology they were meant for is already outdated.”

That said, he highlighted positive progress.

“The government is increasingly open to collaboration with private industries and researchers. The COVID-19 pandemic proved that when urgency is high, solutions can be found quickly. Now, we need to apply that same speed to AI and healthcare policies.”

India vs. Developed Countries: Where Can We Improve?

India has made significant strides in healthcare, but when compared to countries like the U.S., U.K., and Canada, there’s still a wide gap in accessibility.

“We have world-class hospitals in metro cities, but step outside the urban bubble—just 150 kilometers away—and the infrastructure starts to weaken. There’s a major imbalance in healthcare distribution.”

One of the biggest challenges is ensuring that quality healthcare reaches rural and remote areas.

“Health is a fundamental right. Someone in Delhi should have access to the same healthcare as someone in Mizoram or Ladakh. Right now, that’s not the case. But AI, telemedicine, and digital diagnostics can help bridge that gap.”

He believes that startups and disruptive innovations will play a crucial role in transforming India’s healthcare system.

“Yes, many healthcare startups fail, but failure leads to success. We need more startups, more ideas, and most importantly, faster adoption of these innovations by the government.”

A Message to Everyone: Make Health a Habit

Before concluding, Dr. Gaur left an important message for all listeners:

“Healthcare is not just the responsibility of doctors or policymakers. It’s a collective effort. Educate yourself, take care of your health, and build a culture where wellness is a habit, not just a vertical industry.“His words serve as a powerful reminder: healthcare isn’t just about medicine—it’s about community, awareness, and a shared commitment to well-being.

Composed By

InnoHEALTH magazine digital team

Listen to the full podcast on our YouTube channel: https://www.youtube.com/watch?v=4lLRLG9k3L0

The post Beyond Metro Cities: How AI Can Bring Healthcare to Rural India appeared first on InnoHEALTH magazine.

Thyroid gland’s entity might be small but it has a profound impact on human health. The butterfly-shaped organ located at the base of the throat produces hormones that regulate metabolism, growth, and development throughout the body. When the thyroid malfunctions, it can lead to a cascade of health issues affecting nearly every bodily system. In recent years, research has also uncovered important genetic factors that influence thyroid function and disorder risk. This article will explore the critical role of the thyroid gland, common thyroid disorders and their symptoms, and the understanding of genetic influences on thyroid health.

Essential Functions of thyroid

The thyroid gland produces two main hormones: thyroxine (T4) and triiodothyronine (T3). These hormones play crucial roles in:

1. Metabolism: Thyroid hormones regulate how quickly the body burns calories and uses energy.

2. Growth and development: They are essential for proper physical and neurological development, especially in children and adolescents.

3. Body temperature: Thyroid hormones help maintain normal body temperature.

4. Heart rate: They influence how fast the heart beats.

5. Nervous system function: Thyroid hormones affect nerve signal transmission and cognitive function.

6. Reproductive health: They impact fertility and menstrual cycles in women.

Given these wide-ranging effects, it’s clear why thyroid dysfunction can have such significant consequences for overall health.

Common Thyroid Disorders

Thyroid disorders generally fall into two main categories: hypothyroidism (underactive thyroid) and hyperthyroidism (overactive thyroid). Each of these conditions can stem from various causes and presents distinct symptoms.

Hypothyroidism

In hypothyroidism, the thyroid gland doesn’t produce enough hormones. This leads to a general slowing of bodily functions. Common symptoms include:

– Fatigue and weakness

– Weight gain

– Increased sensitivity to cold

– Dry skin and hair

– Depression

– Constipation

– Muscle aches and stiffness

– Irregular or heavy menstrual periods

Hyperthyroidism

– Rapid heartbeat

– Nervousness and anxiety

– Increased sweating

– Weight loss despite increased appetite

– Tremors in hands and fingers

– Sleep difficulties

– More frequent bowel movements

– Enlarged thyroid gland (goiter)

Long-Term Effects of Thyroid Dysfunction

If left untreated, both hypothyroidism and hyperthyroidism can lead to serious health complications.

Untreated hypothyroidism may result in:

– Goiter (enlarged thyroid gland)

– Heart problems, including increased risk of heart disease and heart failure

– Mental health issues, such as depression and cognitive decline

– Peripheral neuropathy (nerve damage in extremities)

– Myxedema, a rare but life-threatening condition characterized by severe drowsiness, unconsciousness, and lowered body temperature

Uncontrolled hyperthyroidism can lead to:

– Heart rhythm disorders, including atrial fibrillation

– Osteoporosis due to accelerated bone loss

– Eye problems, particularly in Graves’ disease

– Thyrotoxic crisis, a life-threatening condition involving extremely high thyroid hormone levels

The Genetic Connection

While lifestyle factors and environmental exposures play roles in thyroid health, genetics also significantly influence thyroid function and disorder risk. Recent research has shed light on several genetic factors:

1. Hereditary Risk: Both hypothyroidism and hyperthyroidism tend to run in families. Having a close relative with a thyroid disorder increases an individual’s risk.

2. Autoimmune Susceptibility: Genes associated with autoimmune disorders, including those affecting the thyroid like Hashimoto’s thyroiditis and Graves’ disease, have been identified. These include variations in genes involved in immune system regulation, such as the HLA (Human Leukocyte Antigen) gene complex.

3. Thyroid Hormone Production: Mutations in genes involved in thyroid hormone synthesis, such as the thyroglobulin (TG) gene and the thyroid peroxidase (TPO) gene, can lead to congenital hypothyroidism.

4. Thyroid Cancer Risk: Certain genetic mutations, like those in the RET gene, are associated with increased risk of medullary thyroid cancer.

5. Iodine Metabolism: Variations in genes involved in iodine uptake and utilization can affect thyroid function. For example, mutations in the sodium/iodide symporter (NIS) gene can impair iodine transport into thyroid cells.

6. Thyroid Hormone Receptors: Genetic variations affecting thyroid hormone receptors can lead to thyroid hormone resistance, a rare condition where the body’s tissues are less responsive to thyroid hormones.

Understanding these genetic factors is crucial for several reasons:

– Improved Risk Assessment: Genetic testing can help identify individuals at higher risk for thyroid disorders, allowing for earlier intervention and monitoring.

– Personalized Treatment: Knowledge of a patient’s genetic profile may help tailor treatment approaches, potentially improving outcomes.

– Family Planning: Couples with a family history of thyroid disorders can benefit from genetic counseling to understand potential risks to their offspring.

– Drug Development: Insights into the genetic basis of thyroid disorders are paving the way for new, targeted therapies.

Diagnosis and Treatment

Given the wide-ranging effects of thyroid disorders and their potential genetic components, proper diagnosis and treatment are crucial. Diagnosis typically involves:

1. Blood tests to measure thyroid hormone levels (TSH, T4, T3)

2. Physical examination, including palpation of the thyroid gland

3. Imaging tests like ultrasound or radioiodine scans

4. In some cases, fine-needle aspiration biopsy to check for cancer

Treatment approaches vary depending on the specific disorder but may include:

– Hormone replacement therapy for hypothyroidism

– Anti-thyroid medications, radioiodine therapy, or surgery for hyperthyroidism

– In some cases, thyroid cancer treatment involving surgery, radioiodine therapy, and targeted drug therapies

The Future of Thyroid Care

As our understanding of thyroid disorders and their genetic underpinnings grows, the future of thyroid care looks promising. Emerging areas of research and potential advancements include:

1. Precision Medicine: Tailoring treatments based on an individual’s genetic profile and specific type of thyroid dysfunction.

2. Gene Therapy: Potential future treatments could involve correcting genetic defects associated with thyroid disorders.

3. Improved Autoimmune Treatments: Better understanding of the genetic basis of autoimmune thyroid disorders may lead to more targeted therapies that address the root cause of the immune system malfunction.

4. Advanced Diagnostic Tools: Development of more sensitive and specific tests for early detection of thyroid dysfunction and related complications.

5. Preventive Strategies: As we identify more genetic risk factors, we may be able to develop preventive measures for high-risk individuals.

The thyroid gland, though small, plays a vital role in maintaining overall health. Thyroid disorders can have far-reaching effects on the body, impacting everything from metabolism to heart function and mental health. The growing understanding of genetic influences on thyroid function not only helps explain why these disorders often run in families but also opens new avenues for diagnosis, treatment, and prevention. As research continues to unravel the complex interplay between genetics and thyroid health, we can look forward to more personalized and effective approaches to managing thyroid disorders, ultimately improving quality of life for millions of people worldwide.

Author’s Biography

Dr. Soumya Singh, Head of Partnerships at InnovatioCuris, is a healthcare expert with a postgraduate degree in Health and Hospital Management and a background in dentistry. Her articles provide valuable insights into healthcare administration and innovation.

The post The Thyroid Enigma: Unraveling the Genetic Threads of a Metabolic Maestro appeared first on InnoHEALTH magazine.

Gene Editing has always been one of the intriguing topics amongst research scientists who are in constant inquisitiveness to know the enhanced human health. Every other outbreak of a deadly disease drives emphasis of scientific research towards Genomic studies and alteration of even more.  CRISPR-Cas 9 has emerged as a magic potion that has aided in such directed scientific research and studies whilst promising a revolutionary future in human health and medicine. As uncanny as it sounds, CRISPR-Cas9 is an intricate, high throughput technique that is the most sought after amongst Molecular biologists. 

CRISPR stands for Clustered Regularly Interspaced Short Palindromic Repeat and Cas-9 stands for CRISPR associated endonuclease-9. The technique marks a significant genetic tool that helps in substantial modification of genomic sequences of human DNA aiding in correction of potential diseases and genetic defects that an individual might be subjected to. The inspiration for the technique goes well beyond our imagination as something as wide-ranging as CRISPR-Cas9 was adapted based on a self-immune defense mechanism naturally occurring in micro creatures like bacteria against viral attack. The mechanism enables bacterial creatures to capture the viral DNA segments and integrate them into their own in a manner that creates segments known as CRISPR arrays. Then, the bacteria sustains a memory of the viral attack in the form of CRISPR arrays and allows generation of respective RNA segments, recognizing and attaching to the viral DNA which is further spliced by Cas-9 enzyme thus disrupting the virus, during the next attack.  

CRISPR-Cas9 has successfully aided in genome editing in plants giving rise to gene knockout mutants that would, otherwise, have been a great deal of time through conventional breeding methods.

The technique possesses widespread implications in plant science research, specifically crop improvement, which is one of the crucial concerns of today’s era. The crop health is vital for a healthy mankind which necessitates use of such revolutionary genetic tools to enhance crop quality and lessen susceptibility to diseases. CRISPR-Cas9 has successfully aided in genome editing in plants giving rise to gene knockout mutants that would, otherwise, have been a great deal of time through conventional breeding methods. Besides doing wonders in Gene Therapy, CRISPR-Cas9 has proven efficiency in all sorts of genetic alterations such as Gene Activation and Silencing and production of Gene knock-in and knock-out mutants for research purposes.

While hailing this technique for scientific research in Human health and medicine, the potential concerns associated with it can’t be overlooked. The CRISPR-Cas9 based DNA editing, however currently restricts Genome modifications to somatic cells and refrains its implications to germ cells which would significantly infer the changes being passed on to the future generations. But the ever-transforming era that we currently are surviving in, we are not very far from witnessing such techniques being utilized to all possible extents including germ cells for mercenary gains. The possibilities would include enhancing or altering normal human traits (such as height, IQ or skin colour) based on personal preference. Considering the bright prospects that CRISPR-Cas9 in gene editing holds, it is yet another important aspect to establish a well-planned set of ethical rules and regulations or code of conduct to be always implemented in all research workplaces carrying out such intensive research-based techniques. The gene editing technique utilizing CRISPR-Cas9 is though one of the most explored techniques by research scientists but still holds extensive potential in Animal and Plant Biotechnology for improved health of mankind.

“Composed by: Aakriti Sharma, an aspiring Biotechnologist from Delhi, India. She is an avid reader, a passionate writer and a consultant in the IP domain. Belonging to scientific background has always inspired her to embrace even the smallest things in life based on her scientific acumen.”

The post CRISPR-cas 9 And Gene Editing For Human Health appeared first on InnoHEALTH magazine.

With the increasing importance and awareness of healthcare, the quality of care and patient safety have gained global concern. In this context, the need for biomarkers cannot be denied at all. 

What are biomarkers?

The term “biomarker” is a merged/blended word of “biological marker”. Biomarkers indicate the underlying biological processes/events, either normal or abnormal taking place in one’s body. Biological markers might be either present or absent; or might be increased or decreased; or might be present in a changed form. They serve as indicators of an underlying disease process, determining the risk factors associated with a disease process. They provide crucial clues about one’s current health or to future health status. 

Biomarkers can be found in blood, urine, stool, tumor tissue or body fluids. They are produced by the normal cells in response to a disease process or cancer or by the cancer cells itself. Biomarkers are available for various diseases: heart diseases, neurological diseases, renal diseases, infective disorders, genetic disorders, cancers etc.

What are the characteristics of an ideal biomarker?

An ideal biomarker should have the following characteristics:

• It should be sensitive.
• It should correlate with the severity of disease/damage.
• It should express itself early in the disease progression.
• It should be measurable in blood/urine.
• It should be stable in a tissue so that it can be measured even after sometime has passed.
• It should be visible prior to the histopathological changes.
• It should give reproducible results.
• It should be portable, reliable, and immediate.
• It should be precise, sensitive and specific.

What are the types of biomarkers?

A. Molecular, histologic, radiographic or physiologic biomarkers.
B. Molecular biomarkers can be:

• DNA (genes)
• Proteins
• Hormones

C. The other type of classification is:

1. Diagnostic biomarkers: Detects/ confirms the presence /absence of a disease condition. Also helps in identifying the disease subtype and helps in differentiating between different diseases. Example: High sensitivity Troponin assay helps in diagnosing myocardial necrosis.
2. Predictive/ prognostic biomarker: Helps to indicate how a disease may develop in an individual when a disorder is already diagnosed. Also helps to determine which patients might be benefitted from a specific treatment plan. To identify  the likelihood of a clinical event, disease recurrence, or disease progression in patients with a disease.
3. Monitoring biomarker: This type of biomarker can be measured serially in order to assess the status of a disease. Example: Measurement of HbA1C, LDL cholesterol levels, INR (to adjust the dose of anticoagulants) etc.
4. Susceptibility biomarker: It indicates the risk for developing a disease in an individual who is currently not having any disease.
5. Safety biomarker: It is measured before/ after an exposure to a medical intervention to indicate the likelihood, presence or extent of toxicity as an adverse event.
6. Treatment- response biomarker: Helpful to differentiate patients on the basis of their outcomes related to efficacy and side effects (responders/non-responders and patients with/without adverse drug reactions).

Macromolecules such as nucleic acids, genetic alterations, and protein molecules as well as intact cells are utilized as diagnostic biomarkers in breast cancer.

Emerging biomarkers in various fields of Pathology:

1. Myeloid neoplasms biomarkers: 

• Immunohistochemistry (IHC) plays an indispensable role in analyzing biomarkers that play an important role in treatment of patients with myeloid neoplasms.
• Cluster of differentiation markers or CD markers are the most commonly used leukemia biomarkers. CD marker specific antibodies are used for cell sorting, identification and leukemia diagnosis.
• Genetic abnormalities have been correlated with Chronic myeloid leukemia (CML) in the blast phase. The genes involved are as follows: 

1. In regulation of cell apoptosis and proliferation: Epidermal growth factor receptor (EGFR), tumor protein p53 (TP53)
2. In cell adhesion: Catenin beta 1 (CTNNB1),
3. Genes associated with TGF Beta: Transforming growth factor beta 1 and 2 (TGFB1, TGFB2)
4. TNF-alpha pathways: Tumor necrosis factor-alpha (TNFA), Nuclear factor kappa B subunit 1 (NFKB1)
5. MicroRNAs are also involved in CML: miRNA -451 nad miRNA -21

2. Breast cancer biomarkers:

As we all are aware, the three most important markers for breast cancer are:

• Estrogen receptor (ER)
• Progesterone receptor (PR)
• HER 2 neu
• Ki-67
• P53

Some of the single blood-based biomarkers in detecting breast cancer are: Human epidermal growth factor receptor 2 (HER2), Cancer Antigen 15-3 (CA 15-3), Carcinoembryonic Antigen (CEA), MUC1.

Macromolecules such as nucleic acids, genetic alterations, and protein molecules as well as intact cells are utilized as diagnostic biomarkers in breast cancer. Recently it is found that aberrant DNA methylation is linked to breast cancer. MAST1, PRDM14, ZNF177 irregular DNA methylation variants were found and verified as prospective breast cancer molecular indicators. Circulating proteins are used as screening biomarkers such as Trefoil factor TFF1, 2, 3. Micro RNA such as miR-145, 221 and 21 are found in the blood of breast cancer individuals.

3. Colorectal cancers (CRC) biomarkers:

• Tumor based biomarkers: Microsatellite instability (MSI) analysis, BRAF and KRAF mutation analysis. Micro RNAs also contribute to oncogenesis in CRC.
• Stool  biomarkers:  Fecal blood tests (gFOBT) is based on the detection of pseudoperoxidase activity of heme in stool samples due to bleeding in adenomatous / neoplastic lesions. Stool immunohistochemical test (FIT) and detection of Vimentin methylation.
• DNA based tests: Mutations in key genes TP53 and APC
• RNA based tests: Detection of tumor mRNA transcripts such as PTGS2 and MMP7.
• Blood based biomarkers: CEA

4. Endometrial cancer biomarker: K-RAS, HER2/neu, EGFR, PI3KCA, FGFR2, PTEN, p53.
5. Neurodegenerative disease biomarkers: Amyloid beta, Total tau protein, Phosphorylated Tau Protein.
6. Biomarkers in diabetes: HbA1C, fasting plasma glucose (FGP), Oral glucose tolerance test (OGTT). Adiponectin, C-reactive protein (CRP), ferritin and interleukin-2 receptor A.

The use of biomarkers is not only limited to cancer but has a widespread use in all the major fields of medicine and research. “The biomarkers are undoubtedly a boon to clinical practice.”

“Composed by: Dr. Shubha.H.V is a Consultant Pathologist/Lab Head, SRL Diagnostics, Fortis Hospital, Rajajinagar, Bangalore. “

The post Evolving Biomarkers in Pathology: Unveiling new avenues for disease detection and prognosis appeared first on InnoHEALTH magazine.

In the dynamic realm of healthcare, an epoch-making force has emerged to redefine the very essence of medical diagnosis – artificial intelligence (AI). By harnessing cutting-edge technologies such as predictive analytics and AI-powered remote patient monitoring, healthcare professionals stand witness to unprecedented advancements in accuracy, efficiency, and patient-centric care. This article delves into the multifaceted advantages of AI in medical diagnosis, exploring the imminent transformation of the medical industry by 2030. We shall also unveil extraordinary examples that stretch the boundaries of imagination and elucidate how AI’s influence will permeate daily life and shape futuristic cities.

Advantage 1: Unveiling the Potential of Predictive Analytics in Healthcare

The fusion of predictive analytics with AI algorithms has bestowed upon healthcare decision-making an unparalleled impetus, endowing medical professionals with the capacity to anticipate and mitigate adversities before they manifest. Predictive analytics tools painstakingly scrutinize voluminous patient data, unraveling intricate patterns and elusive trends beyond human perception. This enlightenment enables physicians to intercede proactively, sculpt personalized treatment regimens, and elevate patient outcomes to unprecedented heights.

An exemplary manifestation of this lies in the development of state-of-the-art AI algorithms, such as the illustrious Random Forest algorithm. This algorithm, encompassing an ensemble of decision trees, augments prognostic capability, enabling accurate predictions regarding disease progression, identification of high-risk patients, and optimal resource allocation. By harnessing the potential of this algorithm, healthcare providers can streamline resource utilization, intervene with alacrity at critical junctures, thus potentially saving lives and mitigating the burgeoning burden of healthcare expenditure.

Advantage 2: Revolutionizing Healthcare through AI-Powered Remote Patient Monitoring

AI-powered remote patient monitoring heralds an epoch of transformative possibilities, revolutionizing the delivery of healthcare services by furnishing real-time data and cogent analysis. This groundbreaking technology empowers medical professionals with the capacity to conduct remote patient surveillance, transcending physical barriers while optimizing patient care and ameliorating the strain on healthcare facilities. Enveloped in wearable devices, AI algorithms enable incessant monitoring of vital signs, swift detection of anomalies, and instant alerts that demand immediate attention.

Within the realm of AI-powered remote patient monitoring, the integration of sophisticated machine learning algorithms, particularly the lauded Long Short-Term Memory (LSTM) models, assumes paramount importance.

Within the realm of AI-powered remote patient monitoring, the integration of sophisticated machine learning algorithms, particularly the lauded Long Short-Term Memory (LSTM) models, assumes paramount importance. Exhibiting remarkable proficiency in analyzing intricate time-series patient data, LSTM models stand as a vanguard in detecting aberrant patterns within vital signs, thereby facilitating early intervention in critical scenarios. The timely insights thus provided not only enhance patient safety but also impart unprecedented efficacy to clinical decision-making, revolutionizing healthcare delivery as we know it.

The Unfathomable Tapestry of Tomorrow:

Glimpsing through the temporal aperture to 2030, we discern an extraordinary tapestry being woven by the threads of AI within the medical industry. Imagine a world where AI algorithms deftly integrate with medical professionals, synergistically enhancing their expertise to administer precision-guided, personalized healthcare with unparalleled dexterity.

In this not-so-distant future, deep learning algorithms, exemplified by convolutional neural networks (CNNs), unfailingly navigate the intricate labyrinth of medical images, serving as invaluable aids to radiologists by facilitating the detection of elusive anomalies with unassailable precision. Furthermore, the advent of natural language processing algorithms offers the tantalizing prospect of assimilating colossal volumes of medical literature, empowering physicians to remain at the vanguard of the latest research, diagnostic methodologies, and treatment modalities, ensuring unparalleled patient care.

Implications for Daily Life and the Urbane Utopias of Tomorrow:

The impact of AI on daily life is poised to be transformative, extending beyond the precincts of medical facilities. Smart cities, enveloped in the embrace of AI, shall transcend their erstwhile constraints, forging an unbreakable bond between healthcare services and the bleeding-edge technologies that underpin their functioning. Envision a city where AI-powered virtual assistants seamlessly cater to the medical needs of its residents, delivering bespoke health advice, scheduling appointments with precision, and even facilitating remote consultations, thus democratizing access to healthcare.

The impact of AI on daily life is poised to be transformative, extending beyond the precincts of medical facilities.

Transportation, too, shall undergo a tectonic shift within these futuristic cities. Autonomous vehicles, fortified by AI algorithms, shall remain vigilant, continuously assessing the health conditions of passengers in real-time. At the first flicker of a medical emergency, these sentient vehicles shall swiftly alert emergency services, ensuring immediate assistance during the journey. Furthermore, AI-powered robotic companions shall aid individuals with disabilities, deftly assisting them with daily activities and safeguarding their well-being with unrivaled dedication.

With a concluding remark we can say that Artificial intelligence stands poised to reshape the very foundations of medical diagnosis, empowering healthcare professionals with unprecedented tools and capabilities. The amalgamation of predictive analytics and AI-powered remote patient monitoring presents an extraordinary landscape, where accuracy, patient care, and healthcare delivery converge to reach unprecedented heights. As the ticking hands of time propel us towards 2030, AI shall permeate the medical industry, orchestrating an epoch of incomparable transformations, creating a future where compassionate healthcare interlaces seamlessly with the vistas of futuristic technologies. Embrace the mesmerizing potential of AI as it propels us towards an era where miracles unravel as the norm, forging a path to a brighter and healthier tomorrow.

“Composed by: Sagar Pandya is a highly accomplished professional with a Master’s degree in Software Technology and a decade long experience in the software industry, working with renowned multinational corporations, gaining expertise in latest technologies. He is also an author, known for his book “Tales of the Jungle: Fables of Indian Animals and Morals.”

The post The Paradigm Shift: Unleashing the Potential of Artificial Intelligence in Medical Diagnosis and its Revolutionary Impact on the Future of Healthcare appeared first on InnoHEALTH magazine.

The various government initiatives

Although 100 percent FDI through automatic route in medical devices was allowed in 2014, tangible attention of policy makers and bureaucrats appeared only in the last two or three years during and post-pandemic. To strengthen the medical devices sector, the government provided the financial support of 25 crore to Andhra Pradesh MedTech Zone (AMTZ) for a common facility centre. Subsequently, to improve manufacturing in cancer/radiotherapy, imaging devices, renal and cardio-respiratory devices and implants sectors, the production linked incentives scheme for medical devices was initiated in 2020, with an amount of 3,420 crore. Further, the medical devices parks were envisaged by allocating 400 crores for developing the medical devices, research facilities and skilled manpower. By introduction of the medical devices rule 2017, which categories the medical devices in four classes, the quality and efficacy was inducted in this sector.

Moreover, the national pharmaceutical pricing authority swung in action for price capping of knee implant, stents, oxygen concentrator and point of care devices (POC) to reduce the dealer’s margin. To address the rising price of drugs, the government opened 8604 affordable outlets (Jan Aushadhi Kendras) which offer 1451 drugs and 240 types of surgical supplies. Another step towards Universal Health Coverage (UHC), The initiative of the Ayushman Bharat-Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) was pioneered by providing the health insurance of ₹ 5 lakhs per family per year, which covers approximately 50 crore Indian poor and vulnerable families. The forenamed initiatives deserve the praise for notable work by the government, but there is still a necessity to advance medical devices sectors to achieve the vision of affordable and quality care to every stratum of the society.

Areas of improvement and suggestions

Despite various schemes initiated by the government, there is a necessity for a comprehensive and clear roadmap for accelerated and continuous growth in the sector. The urge for improvements in the sector is further stressed by the diversity in the medical devices product segment which demands the wide range of interventions in different areas as underscored below. 

Medical devices regulation: The single window online portal for efficient management of information associated with medical devices is essential. The online portal should allow ease in medical device registration, obtaining import and manufacturing licensing, clearance for clinical investigation to all the stakeholders like Co-WIN portal used for vaccination drive.

Research and Development (R & D) funding: The allocation of dedicated funds to encourage joint research with industry, academic and start-ups is much entailed. The fund should be disbursed for building contemporary laboratories for various evaluations, implant and instruments testing, animal studies, equipment testing etc. for R & D activities. These well-equipped laboratories could lead to enhanced research-oriented collaboration, partnerships and innovation in the domestic medical devices sector. 

Price regulation of medical devices: The projected Indian annual per capita income in 2022 is ₹ 1,78,944 ($2,357.643) which translates ₹14,912 per month. The per month income for quality health services to common people is surely less, thus price regulation of medical devices and hospital charges could play an important role in offering affordable medical devices to all. However, careful understanding of medical devices and well devised strategy for price regulation will be required to maintain stability in the medical devices segment.

The various media reports stressed the shortage of beds in health care centres, which left patients unattended for a long time. This insinuated the need for a good physical foundation for health services and healthcare research for better management and treatment of diseases.

Healthcare Infrastructure: The highly infectious COVID-19 virus has created panic in the healthcare community and its deleterious episodes are continuing. On other hand it has emphasised the need for infrastructure requirements. The various media reports stressed the shortage of beds in health care centres, which left patients unattended for a long time. This insinuated the need for a good physical foundation for health services and healthcare research for better management and treatment of diseases. Thus, the need for contemporary healthcare infrastructure is essential, which may also attract the domestic manufactures to invest in medical devices sectors.

Promoting the collaboration: The idea of industrial cluster near to all academic Institutions, could provide the essential environment for industrial collaboration, The global giant in medical devices technology such as Medtronic, Stryker, GE Healthcare, Philips, and Medtronic shall be encircled in this type of collaboration, it can be easily done since most of them already have centres in India. The triple helix model, commonly used in western countries, shall be tried in this collaboration initiative, where government, industry and Institutes would interact for improvement in the domestic medical devices segment.

Innovation parks: The promotion of the ecosystem that houses the network of institutions, start-ups, clinical setting, funding agencies is much needed. This type of ecosystem is called innovation parks; the medical devices parks should be initiated to provide the impetus to scale up the production, encourage the research to market translation and quality medical devices. These parks shall further have strong association with the Ministry of Human Resource Development to make necessary corrections in National Education Policy 2022, by designing proper course work to meet the domestic medical device industry requirements.

Creating centres of excellence: The further boost in creating centres of excellence at reputed institutes would attract world class faculties to engage them in medical devices which are fit for India. These centres shall be further strengthened to drive the role in product development, validation, certification, promote the safety and efficacy and use of latest technology such as Internet of Things (IoT), Robotics, Telemedicine, Artificial Intelligence (AI) etc in the medical devices sector.

Trained Human Resource: As pre survey from AMTZ the half of the workforce in the medical devices sector are unskilled, signaling the need for skill development programs in this sector. The National Institutes for medical devices like IITs, NITs and IISc could be envisaged. These institutes shall provide training, internship, and hands -on experience in medical devices. Further, association with private players shall be encouraged for research and training funding. The course curriculum in these Institutes shall be drafted such that it brings doctors, technicians, service engineering, scientists, and engineers together like Clinical Engineering, a joint initiative by three Institutions namely IIT Madras, SCTIMST and CMC Vellore to improve the skills in the domain.

Patenting is the formal way to protect innovative ideas.

IP (intellectual property): Patenting is the formal way to protect innovative ideas. Intellectual property right offers various advantages such as, competitive advantages against the established competitors, prevents ideas from theft, and signals to investors about the new technology. Patenting in the Indian medical devices sector shall be encouraged for continuous and stable growth in the sector.

Custom Duty: It is a long pending request from the Indian Medical devices industry to increase the custom duty on imported medical devices. The demand is to increase the custom duty from 0 to 7.5 % to 15% and reduce the GST from 18% to 12%. The call on the request shall be considered after proper evaluations, since on one side it will ease the entry of local medical device manufacturers in the market but on other side it will lead to deficiency of quality and innovative product in market.

Acceptance of personalized treatment: Personalized care often called precision medicine is tailor-made treatment to patients based on their molecular profiling. It is an emerging and exciting treatment approach which envisages an important role in preventing and predicting diseases and educates patients to get the right treatment. There shall be a policy to promote such patient centric treatment initiatives within the country, which will contribute to the growth in the medical devices segment since personalized medicine has over-reliance on diagnostic devices.

The aforementioned suggestions are specific to build a strong foundation for the Indian medical devices sector. These are a few steps to reduce import dependency and realize the self-reliant India. If these areas are stressed for the next decade, India could become the global supplier in medical devices from the status of importer.

Composed by: “Er. Arvind Kumar Prajapati is a Scientist/Engineer at Sree Chitra Tirunal Institute for Medical Sciences and Technology (SCTIMST), Department of Science and Technology, Government of India, Trivandrum, Kerala. He has been closely working in design, development, testing and validation of Knee, Hip, and Spine implants and instruments.”

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